SungJei Kim

Andrey Ignatov, Radu Timofte, Maurizio Denna et al.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

HyeonCheol Moon, JinWoo Jeong, SungJei Kim

Recently, CNN-based SISR has numerous parameters and high computational cost to achieve better performance, limiting its applicability to resource-constrained devices such as mobile. As one of the methods to make the network efficient, Knowledge Distillation (KD), which transfers teacher's useful knowledge to student, is currently being studied. More recently, KD for SISR utilizes Feature Distillation (FD) to minimize the Euclidean distance loss of feature maps between teacher and student networks, but it does not sufficiently consider how to effectively and meaningfully deliver knowledge from teacher to improve the student performance at given network capacity constraints. In this paper, we propose a feature-domain adaptive contrastive distillation (FACD) method for efficiently training lightweight student SISR networks. We show the limitations of the existing FD methods using Euclidean distance loss, and propose a feature-domain contrastive loss that makes a student network learn richer information from the teacher's representation in the feature domain. In addition, we propose an adaptive distillation that selectively applies distillation depending on the conditions of the training patches. The experimental results show that the student EDSR and RCAN networks with the proposed FACD scheme improves not only the PSNR performance of the entire benchmark datasets and scales, but also the subjective image quality compared to the conventional FD approaches.